The Valueerror: per-column arrays must each be 1-dimensional error occurs when we attempt to perform an operation that requires one-dimensional arrays, but your input data is not properly formatted.
How the Valueerror Reproduce?
The following are the examples on how the valueerror occur:
Example 1: Creating a Two-Dimensional Array
Assume that you want to create a two-dimensional array in Python using the NumPy library, but you accidentally pass a one-dimensional array instead.
Here’s an example:
import numpy as np
data = [1, 2, 3, 4, 5]
array_2d = np.array(data, [5, 5])
In this example, the ValueError will be raised because np.array() expects a sequence of arrays as the second argument, but 5 is not a valid array. To fix this, you need to provide a valid shape tuple instead of 5.
Example 2: Concatenating One-Dimensional Arrays
Another example where the ValueError may occur is when you try to concatenate multiple arrays vertically or horizontally, but one or more of the arrays are not one-dimensional.
Let’s take a look an example:
import numpy as np
array1 = np.array([1, 2, 3])
array2 = np.array([[4, 5, 6], [7, 8, 9]])
result = np.concatenate((array1, array2))
In this example, the concatenation operation will raise the ValueError because array2 is a two-dimensional array, while array1 is one-dimensional.
The np.concatenate() function requires all input arrays to have the same number of dimensions.
How to Fix the Valueerror?
Here are the following methods to solve the Valueerror per-column arrays must each be 1-dimensional.
Solution 1: Using the reshape() function
To resolve the ValueError we can use the reshape() function.
For example:
import numpy as np
data = [1, 2, 3, 4, 5]
array_2d = np.array(data).reshape((5, 5))
By using the reshape() function, we correctly defined the desired shape of the array.
In this example, we reshape the one-dimensional data array into a two-dimensional array with a shape of (5, 5).
Solution 2: Using the flatten() method
To resolve the ValueError, make sure that all arrays are one-dimensional before performing the concatenation.
Here’s the modified code in the previous example:
import numpy as np
array1 = np.array([1, 2, 3])
array2 = np.array([[4, 5, 6], [7, 8, 9]])
result = np.concatenate((array1, array2.flatten()))
By using the flatten() method on array2, we convert the two-dimensional array into a one-dimensional array, making it compatible for concatenation with array1.
The error will no longer be occur, and the concatenation will succeed.
Frequently Asked Questions
This error typically occurs when you try to perform an operation that expects one-dimensional arrays, but your input data is not correctly formatted.
To reshape a one-dimensional array into a two-dimensional array, you can use the reshape() function in NumPy.
If you want to concatenate a one-dimensional array with a two-dimensional array, make sure that all arrays involved are one-dimensional.
You can use the flatten() method on the two-dimensional array to convert it into a one-dimensional array before performing the concatenation.
Conclusion
In this article, we discussed the ValueError: Per-Column Arrays Must Each Be 1-Dimensional error and provided practical examples and solutions to help you to resolve it.
By following the solutions provided in this article, we can handle this error effectively and write more powerful code when working with arrays in Python.
